Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

© Springer International Publishing Switzerland 2016. We describe a means through which cells can be accurately counted in 3-D microscopy image data, using only weakly annotated images as input training material. We update an existing 2-D density kernel estimation approach into 3-D and we introduce novel 3-D features which encapsulate the 3-D neighbourhood surrounding each voxel. The proposed 3-D density kernel estimation (DKE-3-D) method, which utilises an ensemble of random decision trees, is computationally efficient and achieves state-of-the-art performance. DKE-3-D avoids the problem of discrete object identification and segmentation, common to many existing 3-D counting techniques, and we show that it outperforms other methods when quantification of densely packed and heterogeneous objects is desired. In this article we successfully apply the technique to two simulated and to two experimentally derived datasets and show that DKE-3-D has great potential in the biomedical sciences and any field where volumetric datasets are used.

Original publication

DOI

10.1007/978-3-319-46604-0_18

Type

Conference paper

Publication Date

01/01/2016

Volume

9913 LNCS

Pages

244 - 255